• International Commerce and Information Review
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• v.15 no.1
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• pp.35-65
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• 2013

In December 2012, the Ministry of Land, Transport and Maritime Affairs and Ministry of Knowledge Economy held a commission and distributed a standardized logistics contract between the shipper and the logistics companies in order to spread and to promote contract standardization. With such background in place, this study examines the leading research on different types and attributions in present logistics contracts in order to propose guidelines for creating contract clauses that would lead to a win-win relationship among the parties involved in the logistics outsourcing relationships. This study further compares and contrasts the concreteness of local and international logistics contracts through case studies, and provides practical thought-provoking points on concretization of clauses on potential risks and additional expenses for local logistics companies when signing logistics contracts. Firstly, the composition and contents of both local and international logistics contracts are similar in the way that both deal with the basic principles between the concerned parties such as the following: contract terms, validity, scope of work, operational procedures, payment terms, and dispute resolutions. Secondly, for flexibility of potential dispute resolution, both logistics contracts define the definition of dispute and follow the classical contractual approach of dispute resolution through third-party arbitration. Thirdly, compared to local contracts, international logistics contracts provide more concretized and specific clauses on the occurrence of potential risks and hazards; on the other hand, compared to international logistics contracts, it seemed that local contracts contained more clauses in favor of the shipper. This research then suggests ideas to eliminate the classic tradition - logistics companies enduring the damages that occur as a result of the structural differences between the shipper and the logistics companies - through efforts to actively negotiate in advance the predictable problems and risks and by reflecting the mutually agreed points in the contract, and further offers guidelines on contract concretization for distribution of standardized logistics contracts in the future.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.11
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• pp.471-480
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• 2023

The Korean construction order volume in South Korea grew significantly from 91.3 trillion won in public orders in 2013 to a total of 212 trillion won in 2021, particularly in the private sector. As the size of the domestic and overseas markets grew, the scale and complexity of EPC (Engineering, Procurement, Construction) projects increased, and risk management of project management and ITB (Invitation to Bid) documents became a critical issue. The time granted to actual construction companies in the bidding process following the EPC project award is not only limited, but also extremely challenging to review all the risk terms in the ITB document due to manpower and cost issues. Previous research attempted to categorize the risk terms in EPC contract documents and detect them based on AI, but there were limitations to practical use due to problems related to data, such as the limit of labeled data utilization and class imbalance. Therefore, this study aims to develop an AI model that can categorize the contract terms based on the FIDIC Yellow 2017(Federation Internationale Des Ingenieurs-Conseils Contract terms) standard in detail, rather than defining and classifying risk terms like previous research. A multi-text classification function is necessary because the contract terms that need to be reviewed in detail may vary depending on the scale and type of the project. To enhance the performance of the multi-text classification model, we developed the ELECTRA PLM (Pre-trained Language Model) capable of efficiently learning the context of text data from the pre-training stage, and conducted a four-step experiment to validate the performance of the model. As a result, the ensemble version of the self-developed ITB-ELECTRA model and Legal-BERT achieved the best performance with a weighted average F1-Score of 76% in the classification of 57 contract terms.